org.apache.spark.ml.regression

Class LinearRegression

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Estimator<M>

org.apache.spark.ml.Predictor<FeaturesType,Learner,M>

org.apache.spark.ml.regression.LinearRegression

All Implemented Interfaces:

java.io.Serializable, Logging, Params

public class LinearRegression
extends Predictor<FeaturesType,Learner,M>
implements Logging

:: Experimental :: Linear regression.

The learning objective is to minimize the squared error, with regularization. The specific squared error loss function used is: L = 1/2n ||A weights - y||^2^

This support multiple types of regularization: - none (a.k.a. ordinary least squares) - L2 (ridge regression) - L1 (Lasso) - L2 + L1 (elastic net)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
LinearRegression()
LinearRegression(String uid)

Method Summary

Methods

Modifier and Type	Method and Description
LinearRegression	setElasticNetParam(double value) Set the ElasticNet mixing parameter.
LinearRegression	setMaxIter(int value) Set the maximum number of iterations.
LinearRegression	setRegParam(double value) Set the regularization parameter.
LinearRegression	setTol(double value) Set the convergence tolerance of iterations.
String	uid()
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType) Validates and transforms the input schema with the provided param map.

Methods inherited from class org.apache.spark.ml.Predictor

copy, fit, setFeaturesCol, setLabelCol, setPredictionCol, transformSchema

Methods inherited from class org.apache.spark.ml.Estimator

fit, fit, fit, fit

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, setDefault, shouldOwn, validateParams

Constructor Detail

LinearRegression

public LinearRegression(String uid)

LinearRegression

public LinearRegression()

Method Detail

uid

public String uid()

setRegParam

public LinearRegression setRegParam(double value)

Set the regularization parameter. Default is 0.0.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setElasticNetParam

public LinearRegression setElasticNetParam(double value)

Set the ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. For 0 < alpha < 1, the penalty is a combination of L1 and L2. Default is 0.0 which is an L2 penalty.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setMaxIter

public LinearRegression setMaxIter(int value)

Set the maximum number of iterations. Default is 100.

Parameters:

value - (undocumented)

Returns:

(undocumented)

setTol

public LinearRegression setTol(double value)

Set the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-6.

Parameters:

value - (undocumented)

Returns:

(undocumented)

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                    boolean fitting,
                                    DataType featuresDataType)

Validates and transforms the input schema with the provided param map.

Parameters:

schema - input schema

fitting - whether this is in fitting

featuresDataType - SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

Returns:

output schema